The situation
The company had built AI deep into its daily operations — and into a growing stack of per-token invoices. Every workload ran against metered cloud APIs: costs scaled linearly with success, sensitive data crossed an external boundary on every call, and capacity, latency and model behaviour were ultimately someone else's decisions. What began as the fastest way to adopt AI had become a structural dependency.
The problem underneath the problem
This is a build-versus-rent decision, and the honest answer changes with scale. Below a certain sustained volume, renting is correct — we say so in diagnosis sessions regularly. This company was past that line: predictable, high-volume workloads where owned hardware pays for itself, data-control requirements that argue against an external boundary, and enough operational maturity to run its own stack. The diagnosis priced the crossover before any hardware was specified.
What we're building
Custom inference infrastructure — hardware specified for the company's actual workload profile rather than a generic GPU order — and the platform layer that makes it usable: model serving, routing, monitoring and fallback. Migration runs workload by workload: each one is benchmarked against its cloud baseline for quality, latency and cost, moved into production on the new stack, and only then followed by the next.
The hard part
Migrating a running business without interrupting it. Every workload being moved is one the company depends on daily, so each migration ships with a rollback path to the cloud API it replaces, and cutover happens only after the on-prem version has matched its baseline in production-shadow mode. The discipline is deliberately boring — which is the point of infrastructure.
Where it stands
The engagement is ongoing: early workloads are in production on the new hardware, the migration queue is moving, and the numbers that matter — cost per workload against the cloud baseline, latency, and how much sensitive data no longer leaves the building — are being measured as each workload lands. Per our own rule, the full before-and-after claims will be published when the after-numbers are in.